Cells, making up every living organism on earth, are extremely complicated biological machines. Much research has been done on how cells function, including how proteins made by cells work together to create biochemical pathways necessary for growth. Recently, computational simulations of biological processes have become possible, such as in the field of systems biology, which takes a holistic view of an organism to elucidate function. Previous research has constructed an accurate "whole-cell" model of E. Coli metabolic function. But, understanding metabolic behavior is still a challenge, especially from external perturbations in the environment or an internal alteration in the form of an insertion of an extra protein. This research uses an E. Coli whole-cell model—containing carbon-dependent metabolic pathways in E. coli— to study how perturbations will affect the pathway. External changes in the resources available to the cell or an internal change to the cell’s components are simulated. In response, specific changes to the metabolic behavior of proteins will be made. These changes are made using optimization algorithms, which minimize the output of a growth “cost” function, which maximizes the growth rate of the cell. This represents the effect of evolution over time for the cell to reach an optimal level of growth. The “Tellurium” software package, using the Python programming language and created by the Sauro lab, was used for this experiment, and a collection of scripts that can be used in Tellurium were made to generalize the reproduction of this process to any model, and to visualize the results. This research can give greater insight to how cellular metabolic processes as a whole behave, and can give scientists working with in vivo experiments better predictions of the consequences of perturbations, externally or internally, on a cell.